How to take a picture of a black hole | Katie Bouman

3,361,362 views ・ 2017-04-28

TED


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譯者: Debra Liu 審譯者: Wilde Luo
00:13
In the movie "Interstellar,"
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在「星際效應」這部電影中,
00:15
we get an up-close look at a supermassive black hole.
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我們更近距離地看到了 超質量黑洞。
00:18
Set against a backdrop of bright gas,
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在明亮氣體的背景下,
00:20
the black hole's massive gravitational pull
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黑洞的巨大引力
00:22
bends light into a ring.
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使光線形成戒指般的環狀。
00:24
However, this isn't a real photograph,
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但是,這不是張真實的照片,
00:26
but a computer graphic rendering --
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而是電腦圖像的呈現,
00:28
an artistic interpretation of what a black hole might look like.
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是對黑洞可能的呈像的 藝術化的演繹。
00:32
A hundred years ago,
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一百年前,
00:33
Albert Einstein first published his theory of general relativity.
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愛因斯坦首先發表了他的相對論。
00:37
In the years since then,
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那之後的幾年,
00:38
scientists have provided a lot of evidence in support of it.
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科學家提供了很多證據支持他的理論。
00:41
But one thing predicted from this theory, black holes,
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但是從他的理論中預測到的黑洞
00:44
still have not been directly observed.
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仍然無法有直接證據證實。
00:47
Although we have some idea as to what a black hole might look like,
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雖然我們對於黑洞的呈像有一些想法,
00:50
we've never actually taken a picture of one before.
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但是我們從來沒有真正 拍攝過一張黑洞的相片。
00:53
However, you might be surprised to know that that may soon change.
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也許你會驚訝於這種困境即將改變。
00:57
We may be seeing our first picture of a black hole in the next couple years.
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我們在未來幾年內也許 可以得到第一張黑洞的相片。
01:01
Getting this first picture will come down to an international team of scientists,
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國際的科學家團隊 將會獲得這第一張圖片,
01:05
an Earth-sized telescope
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透過地球大小般的望遠鏡,
01:07
and an algorithm that puts together the final picture.
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和一個演算方法,獲得最後這張圖片。
01:10
Although I won't be able to show you a real picture of a black hole today,
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雖然,我今天無法讓大家看到 黑洞真正的照片,
01:13
I'd like to give you a brief glimpse into the effort involved
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但是,我想要簡單地向各位說明一下
01:16
in getting that first picture.
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獲得這首張照片所付出的努力。
01:19
My name is Katie Bouman,
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我是 Katie Bouman ,
01:20
and I'm a PhD student at MIT.
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一名麻省理工學院博士生。
01:23
I do research in a computer science lab
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我在電腦科學實驗室做研究,
01:25
that works on making computers see through images and video.
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讓電腦透過影像及影片, 能夠「看見」、識別。
01:28
But although I'm not an astronomer,
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雖然我不是天文學家,
01:31
today I'd like to show you
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但是,我現在要讓大家看的是
01:32
how I've been able to contribute to this exciting project.
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我如何投入這令人興奮的專案。
01:35
If you go out past the bright city lights tonight,
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如果今晚你們離開了城市明亮的燈光,
01:38
you may just be lucky enough to see a stunning view
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可能運氣夠好,
可以看到銀河系美麗的影像。
01:40
of the Milky Way Galaxy.
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01:42
And if you could zoom past millions of stars,
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如果你的視野能夠穿越數百萬顆星星,
01:44
26,000 light-years toward the heart of the spiraling Milky Way,
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向着銀河的螺旋中心前進 26,000 光年,
01:48
we'd eventually reach a cluster of stars right at the center.
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最後會在中心點遇到一群星星。
01:51
Peering past all the galactic dust with infrared telescopes,
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天文學家使用紅外線望遠鏡 透過銀河系塵埃
01:55
astronomers have watched these stars for over 16 years.
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觀察這些星星, 已經超過了 16 年。
01:59
But it's what they don't see that is the most spectacular.
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但是,最為壯觀的東西, 卻是他們無法看見的。
02:02
These stars seem to orbit an invisible object.
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這些星星似乎繞著一個 隱形的物體運轉著。
02:05
By tracking the paths of these stars,
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藉由追蹤這些星星的軌跡,
02:08
astronomers have concluded
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天文學家得到一個結論:
02:09
that the only thing small and heavy enough to cause this motion
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只有一個又小又重的物體 才能夠造成這樣的運動軌跡,
02:12
is a supermassive black hole --
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那就是超質量黑洞,
02:14
an object so dense that it sucks up anything that ventures too close --
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它的密度高到能夠吸收 所有敢於近距離靠近它的東西,
02:18
even light.
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連光線也不例外。
02:20
But what happens if we were to zoom in even further?
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但是,如果我們將影像放大, 會發生什麼事呢?
02:23
Is it possible to see something that, by definition, is impossible to see?
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有沒有可能看到那些 原本被定義為看不見的東西呢?
02:28
Well, it turns out that if we were to zoom in at radio wavelengths,
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事實顯示,如果我們 以無線電波長的尺度放大,
02:31
we'd expect to see a ring of light
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我們預期可以看到一個光環,
02:33
caused by the gravitational lensing of hot plasma
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它是由黑洞旁 高速移動的熱離子體的
02:36
zipping around the black hole.
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「引力透鏡」效應形成。
02:37
In other words,
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換句話說,
02:39
the black hole casts a shadow on this backdrop of bright material,
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黑洞在明亮物質的背景下投射出陰影,
02:42
carving out a sphere of darkness.
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刻畫出黑色的球體。
02:44
This bright ring reveals the black hole's event horizon,
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這個光環揭露了黑洞的表面界限,
02:47
where the gravitational pull becomes so great
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在那個地方,引力拉扯的力量很大,
02:50
that not even light can escape.
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連光線都無法逃脫。
02:51
Einstein's equations predict the size and shape of this ring,
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愛因斯坦方程式預測了 這個光環的大小與形狀,
02:54
so taking a picture of it wouldn't only be really cool,
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所以拍攝黑洞的相片不只是很酷,
02:57
it would also help to verify that these equations hold
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它也有助於驗證這些方程式
03:00
in the extreme conditions around the black hole.
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能在黑洞附近這樣的 極端環境下成立。
03:02
However, this black hole is so far away from us,
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但是,這個黑洞距離我們非常遙遠,
03:05
that from Earth, this ring appears incredibly small --
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從地球看過去, 這個光環是不可思議的小,
03:08
the same size to us as an orange on the surface of the moon.
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就像是月球表面的 一個橘子那樣的小。
03:12
That makes taking a picture of it extremely difficult.
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所以拍攝黑洞的相片是極其困難的。
03:16
Why is that?
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為什麼呢?
03:18
Well, it all comes down to a simple equation.
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因為,這所有的一切 可以歸結於一個簡單的方程式。
03:21
Due to a phenomenon called diffraction,
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由於「衍射」現象,
03:24
there are fundamental limits
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我們所能觀察到的最小物體,
03:25
to the smallest objects that we can possibly see.
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是有限小的, 我們無法洞察更小的結構。
03:28
This governing equation says that in order to see smaller and smaller,
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這個方程式說, 為了要看到越來越小的物體,
03:32
we need to make our telescope bigger and bigger.
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我們必須製作越來越大的望遠鏡。
03:35
But even with the most powerful optical telescopes here on Earth,
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但是,即使透過地球上 最強大的光學望遠鏡,
03:38
we can't even get close to the resolution necessary
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我們還是遠遠達不到
03:40
to image on the surface of the moon.
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拍攝月球表面的影像所需要的解析度。
03:42
In fact, here I show one of the highest resolution images ever taken
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事實上,請大家看看這張從地球拍攝的
解析度最高的月球照片之一,
03:46
of the moon from Earth.
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03:47
It contains roughly 13,000 pixels,
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這張相片大約有一萬三千像素,
03:50
and yet each pixel would contain over 1.5 million oranges.
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而每一個像素可包含 超過 150 萬個橘子。
03:55
So how big of a telescope do we need
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那麼,為了要看到月球表面的橘子,
03:57
in order to see an orange on the surface of the moon
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我們需要多大的望遠鏡呢?
04:00
and, by extension, our black hole?
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再者,為了要看到黑洞?
04:02
Well, it turns out that by crunching the numbers,
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事實證明,透過大量運算,
04:04
you can easily calculate that we would need a telescope
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我們可以很容易地計算出 我們所需要的望遠鏡
04:07
the size of the entire Earth.
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必須是整個地球那麼大。
04:08
(Laughter)
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(笑聲)
04:09
If we could build this Earth-sized telescope,
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如果我們建造出地球般大小的望遠鏡,
04:12
we could just start to make out that distinctive ring of light
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我們馬上就可以探測出一個獨特光環,
04:14
indicative of the black hole's event horizon.
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它表明了黑洞的表面界限。
04:17
Although this picture wouldn't contain all the detail we see
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雖然這張相片沒有包含所有細節,
04:20
in computer graphic renderings,
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像我們在電腦圖形渲染上看到的那樣,
04:21
it would allow us to safely get our first glimpse
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但是,至少我們可以安全地
04:23
of the immediate environment around a black hole.
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對黑洞附近的環境瞥上一眼。
04:26
However, as you can imagine,
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然而,如同大家想像的,
04:28
building a single-dish telescope the size of the Earth is impossible.
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建造一個地球大小的 單碟望遠鏡是不可能的。
04:31
But in the famous words of Mick Jagger,
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但是在 Mick Jagger 的名言中:
04:33
"You can't always get what you want,
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「你無法一直得到你所想要的,
04:35
but if you try sometimes, you just might find
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但是如果你去嘗試,你可能會發現
04:37
you get what you need."
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你得到了你所需要的。」
04:38
And by connecting telescopes from around the world,
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藉由連結世界各地的望遠鏡,
04:41
an international collaboration called the Event Horizon Telescope
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名為「事件視界望遠鏡」的國際組織
04:44
is creating a computational telescope the size of the Earth,
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正著手創建一個地球大小的 計算型望遠鏡,
04:48
capable of resolving structure
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它能夠解析黑洞的
04:49
on the scale of a black hole's event horizon.
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表面界限的結構。
04:51
This network of telescopes is scheduled to take its very first picture
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這個望遠鏡網路預計明年
04:55
of a black hole next year.
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拍攝黑洞的第一張相片。
04:57
Each telescope in the worldwide network works together.
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世界各地的望遠鏡網路同時運作。
05:00
Linked through the precise timing of atomic clocks,
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透過原子鐘的精準時間鏈結,
05:03
teams of researchers at each of the sites freeze light
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每個地點的研究團隊 藉由蒐集數千兆兆字節的數據
05:05
by collecting thousands of terabytes of data.
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將光線「定格」。
05:08
This data is then processed in a lab right here in Massachusetts.
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麻薩諸塞州這裡的實驗室 接下來處理這些資料。
05:13
So how does this even work?
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那麼,這些資料是如何運作的呢?
05:15
Remember if we want to see the black hole in the center of our galaxy,
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還記得嗎?如果我們想要 看清在銀河中間的黑洞,
05:19
we need to build this impossibly large Earth-sized telescope?
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我們就需要建造地球大小的 望遠鏡,這是不現實的。
05:22
For just a second, let's pretend we could build
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等一下,假設我們能夠建造
05:24
a telescope the size of the Earth.
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地球般大小的望遠鏡。
05:26
This would be a little bit like turning the Earth
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就有點像將地球
05:28
into a giant spinning disco ball.
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想像成舞廳裡的迪斯可旋轉球。
05:30
Each individual mirror would collect light
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每一面鏡子會蒐集光線,
05:32
that we could then combine together to make a picture.
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然後我們能將這些 影像整合成一張圖片。
05:35
However, now let's say we remove most of those mirrors
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但是,現在讓我們 移除大多數的鏡子,
05:38
so only a few remained.
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只剩下少數幾個。
05:40
We could still try to combine this information together,
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我們仍可試著整合這些資訊,
05:43
but now there are a lot of holes.
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但是,現在只能看到很多「孔洞」。
05:45
These remaining mirrors represent the locations where we have telescopes.
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這些剩下的鏡子代表 那些有望遠鏡的地方。
05:49
This is an incredibly small number of measurements to make a picture from.
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測量數據少之又少, 甚至無法形成一張圖片。
05:53
But although we only collect light at a few telescope locations,
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雖然我們只在少數 有望遠鏡的地方蒐集光線,
05:57
as the Earth rotates, we get to see other new measurements.
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地球旋轉時,我們可以 獲得一些新的測量數據。
06:00
In other words, as the disco ball spins, those mirrors change locations
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換句話說,就像迪斯可球旋轉時, 那些鏡子也會改變位置,
06:04
and we get to observe different parts of the image.
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我們得以觀察不同面向的影像。
06:07
The imaging algorithms we develop fill in the missing gaps of the disco ball
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我們所開發的成像算法填補了 「迪斯可球」的不可見縫隙,
06:11
in order to reconstruct the underlying black hole image.
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目的在重建黑洞的相片。
06:14
If we had telescopes located everywhere on the globe --
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如果地球的每個地方都有望遠鏡,
06:17
in other words, the entire disco ball --
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也就是整個迪斯可球佈滿了鏡子,
06:19
this would be trivial.
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這是最簡潔、理想的情況。
06:20
However, we only see a few samples, and for that reason,
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但是,我們只看得到 某些局部的成像,因此,
06:23
there are an infinite number of possible images
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有無數可能的相片
06:26
that are perfectly consistent with our telescope measurements.
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可以與現有望遠鏡的 局部成像相吻合。
06:29
However, not all images are created equal.
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當然,並不是每一張「相片」的 優先級別都相同。
06:32
Some of those images look more like what we think of as images than others.
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有些相片比別的 更近似我們所想像的。
06:37
And so, my role in helping to take the first image of a black hole
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因此,為了協助拍攝黑洞 第一張相片,我的任務就是
06:40
is to design algorithms that find the most reasonable image
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設計發現最合理影像的演算法,
06:43
that also fits the telescope measurements.
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當然也必須符合望遠鏡的量測數據。
06:46
Just as a forensic sketch artist uses limited descriptions
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就像法庭的素描家一樣, 利用有限的相貌描述以及
06:50
to piece together a picture using their knowledge of face structure,
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他們對於臉部結構的知識, 將表現相貌特點的圖片拼湊出來,
06:54
the imaging algorithms I develop use our limited telescope data
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我開發的影像演算法 使用有限的望遠鏡資料
06:57
to guide us to a picture that also looks like stuff in our universe.
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為我們生成這種影像: 類似於宇宙中的事物的影像。
07:01
Using these algorithms, we're able to piece together pictures
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利用這些演算法, 讓我們能夠利用零零散散的資料
07:05
from this sparse, noisy data.
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拼湊出黑洞可能的樣子。
07:07
So here I show a sample reconstruction done using simulated data,
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在這裡,讓大家看一個利用模擬資料 重建的影像樣本,
07:12
when we pretend to point our telescopes
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這是我們假設將望遠鏡指向
07:14
to the black hole in the center of our galaxy.
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銀河系中心的黑洞時所得到的。
07:16
Although this is just a simulation, reconstruction such as this give us hope
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雖然這只是一個模擬, 但是這讓我們充滿了希望:
07:21
that we'll soon be able to reliably take the first image of a black hole
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我們能夠仰賴這樣的模擬演算法, 很快地得到黑洞的第一張相片,
07:24
and from it, determine the size of its ring.
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同時也能計算「光環」的大小。
07:28
Although I'd love to go on about all the details of this algorithm,
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雖然我很樂意繼續說明 這個演算法的所有細節,
07:31
luckily for you, I don't have the time.
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但由於時間不夠,所以 你們也不用費腦子聽了。
07:33
But I'd still like to give you a brief idea
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但是,我還是很樂意 跟大家做個簡短的說明:
07:35
of how we define what our universe looks like,
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我們如何定義宇宙看起來像什麼?
07:37
and how we use this to reconstruct and verify our results.
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以及我們如何 利用這個演算法重建並驗證結果。
07:42
Since there are an infinite number of possible images
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因為有無數可能的影像
07:44
that perfectly explain our telescope measurements,
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與地球上望遠鏡的量測完全符合,
07:47
we have to choose between them in some way.
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我們必須在它們之間 找個方法進行挑選。
07:49
We do this by ranking the images
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我們對影像進行打分,
07:51
based upon how likely they are to be the black hole image,
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打分的根據是:看起來有多像黑洞,
07:54
and then choosing the one that's most likely.
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然後選擇最像的影像。
07:57
So what do I mean by this exactly?
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那麼,這到底是什麼意思呢?
07:59
Let's say we were trying to make a model
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假設我們試著建立一個模型,
08:01
that told us how likely an image were to appear on Facebook.
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它告訴我們這個影像在 Facebook 上出現的可能性。
08:05
We'd probably want the model to say
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我們希望這個模型會這樣判斷:
08:06
it's pretty unlikely that someone would post this noise image on the left,
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大家應該不太可能會上傳 像左邊這張亂亂的圖,
08:10
and pretty likely that someone would post a selfie
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而比較可能會上傳自拍照,
08:12
like this one on the right.
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像右邊這張。
08:14
The image in the middle is blurry,
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中間這張圖像片是模糊的,
08:15
so even though it's more likely we'd see it on Facebook
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即使模糊,和亂亂的圖像比較的話, 我們還是很有可能
08:18
compared to the noise image,
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在 Facebook 上看到,
08:19
it's probably less likely we'd see it compared to the selfie.
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只不過不如自拍照那樣常見。
08:22
But when it comes to images from the black hole,
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但是,如果是黑洞的影像,
08:25
we're posed with a real conundrum: we've never seen a black hole before.
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我們遇到一個真正的難題: 我們從來沒見過黑洞的樣子。
08:28
In that case, what is a likely black hole image,
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在這種情況下, 黑洞可能的影像是什麼?
08:31
and what should we assume about the structure of black holes?
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我們應該假設黑洞的結構是什麼?
08:33
We could try to use images from simulations we've done,
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我們可能會試著使用 之前生成的模擬結果,
08:36
like the image of the black hole from "Interstellar,"
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像「星際效應」裡的黑洞影像,
08:39
but if we did this, it could cause some serious problems.
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但是,如果這樣做的話, 可能會造成一些嚴重的問題。
08:42
What would happen if Einstein's theories didn't hold?
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如果愛因斯坦的理論不適用的話, 會發生什麼事?
08:45
We'd still want to reconstruct an accurate picture of what was going on.
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我們還是想要重建 一個準確的圖像。
08:49
If we bake Einstein's equations too much into our algorithms,
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如果將太多愛因斯坦的方程式 融入我們的演算法中,
08:52
we'll just end up seeing what we expect to see.
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最後只會得到我們期望的結果, 而不一定是事實。
08:55
In other words, we want to leave the option open
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換句話說,我們不能 貿然確定實際情況如何,
08:58
for there being a giant elephant at the center of our galaxy.
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因為銀河系中央有一隻巨象。
09:00
(Laughter)
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(笑聲)
09:02
Different types of images have very distinct features.
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不同類型的影像有著 各自非常顯著的特徵。
09:05
We can easily tell the difference between black hole simulation images
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我們可以很容易地區分 黑洞模擬影像
09:08
and images we take every day here on Earth.
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以及我們在地球上日常生活中的照片。
09:10
We need a way to tell our algorithms what images look like
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我們需要一種方法來告訴演算法 影像看起來像什麼,
09:14
without imposing one type of image's features too much.
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而不是去強加特定一種影像的特徵給它。
09:17
One way we can try to get around this
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我們可以用一個方法 試著解決這個問題:
09:19
is by imposing the features of different kinds of images
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通過導入不同類型影像的特徵 讓演算法重建影像,
09:22
and seeing how the type of image we assume affects our reconstructions.
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然後觀察預先假設的影像類型 如何影響我們重建的影像。
09:27
If all images' types produce a very similar-looking image,
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如果所有不同類型的影像特徵 產生的結果都很類似,
09:31
then we can start to become more confident
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那麼我們可以充滿信心地說:
09:33
that the image assumptions we're making are not biasing this picture that much.
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對於這個影像所做的假設 沒有與事實偏差太多。
09:37
This is a little bit like giving the same description
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這有點像是將相同的相貌描述
09:40
to three different sketch artists from all around the world.
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提供給三個來自世界各地不同的素描家,
09:43
If they all produce a very similar-looking face,
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如果他們都畫出很相像的臉,
09:46
then we can start to become confident
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那麼我們可以充滿信心地說:
09:48
that they're not imposing their own cultural biases on the drawings.
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他們的作品沒有受到 本人的文化偏見的影響。
09:51
One way we can try to impose different image features
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我們導入不同類型影像 的特徵的一個方法
09:55
is by using pieces of existing images.
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就是藉由現存的影像去拼湊。
09:58
So we take a large collection of images,
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所以我們要蒐集大量的影像,
10:00
and we break them down into their little image patches.
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然後將它們分解成許多碎片。
10:03
We then can treat each image patch a little bit like pieces of a puzzle.
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之後我們可以把這些碎片 當作拼圖的碎片。
10:07
And we use commonly seen puzzle pieces to piece together an image
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我們使用常見的「碎片」拼湊成圖片,
10:11
that also fits our telescope measurements.
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這張圖片當然也要 符合望遠鏡的量測數據。
10:15
Different types of images have very distinctive sets of puzzle pieces.
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不同類型的影像有其獨特的拼圖碎片。
10:18
So what happens when we take the same data
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所以,當我們利用相同的數據資料
10:21
but we use different sets of puzzle pieces to reconstruct the image?
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卻使用不同類型的拼圖碎片 來重建這個影像,會發生什麼事?
10:25
Let's first start with black hole image simulation puzzle pieces.
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讓我們先從黑洞模擬 圖像的拼圖碎片開始。
10:30
OK, this looks reasonable.
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好的,這看起來很合理。
10:32
This looks like what we expect a black hole to look like.
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這看起來像我們 所期待的黑洞的樣子。
10:34
But did we just get it
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但是,僅僅是導入了 一些些黑洞模擬影像的碎片,
10:36
because we just fed it little pieces of black hole simulation images?
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我們就得出了結果嗎?
10:39
Let's try another set of puzzle pieces
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讓我們來試試另一組拼圖,
10:41
from astronomical, non-black hole objects.
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這些是天文學影像的拼圖,不是黑洞的。
10:44
OK, we get a similar-looking image.
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沒錯,我們得到一個類似的影像。
10:47
And then how about pieces from everyday images,
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那麼如果是日常生活的影像呢?
10:49
like the images you take with your own personal camera?
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就像用相機所照的照片一樣?
10:53
Great, we see the same image.
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很好,我們得到相同的影像。
10:55
When we get the same image from all different sets of puzzle pieces,
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當我們從不同類型的拼圖 得到相同的影像,
10:58
then we can start to become more confident
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我們更有信心了,
11:00
that the image assumptions we're making
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我們所假定的影像
11:02
aren't biasing the final image we get too much.
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和我們最後得到的影像 並沒有差距太多。
11:05
Another thing we can do is take the same set of puzzle pieces,
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我們可以做的另一件事 就是使用同一組拼圖,
11:09
such as the ones derived from everyday images,
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比如日常生活中的影像碎片,
11:11
and use them to reconstruct many different kinds of source images.
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並利用它們來重組 各種不同素材來源的影像。
11:15
So in our simulations,
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那麼,在模擬實驗當中,
11:16
we pretend a black hole looks like astronomical non-black hole objects,
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我們假設黑洞看起來就像是 天文學裡那些非黑洞的物體,
11:20
as well as everyday images like the elephant in the center of our galaxy.
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或者又把它看成「銀河系中央的大象」 這樣的日常生活影像。
11:24
When the results of our algorithms on the bottom look very similar
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我們下方的演算結果
11:27
to the simulation's truth image on top,
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和上方的模擬實驗中的真實影像很相像,
11:29
then we can start to become more confident in our algorithms.
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我們就可以對我們的演算法更有信心。
11:32
And I really want to emphasize here
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我真的想要強調這一點:
11:34
that all of these pictures were created
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這些所有的圖片都是
11:36
by piecing together little pieces of everyday photographs,
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由日常生活照片的碎片 拼湊出來的,
11:39
like you'd take with your own personal camera.
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就是那種用私人相機照出來的照片。
11:41
So an image of a black hole we've never seen before
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我們之前從沒看過黑洞的相片,
11:45
may eventually be created by piecing together pictures we see all the time
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但最後黑洞的相片也許是由我們 常常看到的日常生活照片拼湊出來的:
11:49
of people, buildings, trees, cats and dogs.
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人像、建築物、樹木、貓、狗等等。
11:51
Imaging ideas like this will make it possible for us
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這些成像方法讓我們能夠
11:54
to take our very first pictures of a black hole,
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拍攝出黑洞的第一張相片,
11:57
and hopefully, verify those famous theories
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我們同時也希望 能夠驗證那些著名的理論,
11:59
on which scientists rely on a daily basis.
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那些科學家平常所依賴的理論。
12:02
But of course, getting imaging ideas like this working
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當然,提出這些成像的方法與理論,
12:04
would never have been possible without the amazing team of researchers
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沒有一個驚人的研究團隊 是不可能達到這種成果的,
12:08
that I have the privilege to work with.
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我很榮幸身為這個團隊的一員。
12:10
It still amazes me
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我對這件事感到驚異:
12:11
that although I began this project with no background in astrophysics,
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雖然我沒有任何天文物理的背景 而加入這個專案,
12:14
what we have achieved through this unique collaboration
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我們透過這獨特的合作所得到的,
12:17
could result in the very first images of a black hole.
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能夠獲得第一張黑洞的相片。
12:20
But big projects like the Event Horizon Telescope
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但是像「事件視界望遠鏡」 這樣的大專案,
12:22
are successful due to all the interdisciplinary expertise
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多虧有跨學科領域的專業知識而成功,
12:25
different people bring to the table.
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不同的專家共同合作着。
12:27
We're a melting pot of astronomers,
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我們像是個熔爐,集結了天文學家、
12:29
physicists, mathematicians and engineers.
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物理學家、數學家和工程師。
12:31
This is what will make it soon possible
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這就是我們讓不可思議的事情
12:34
to achieve something once thought impossible.
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快速實現的原因。
12:36
I'd like to encourage all of you to go out
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我很想鼓勵大家
12:39
and help push the boundaries of science,
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去協助推動科學的前沿,
12:41
even if it may at first seem as mysterious to you as a black hole.
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即使第一步可能像黑洞那樣神秘。
12:45
Thank you.
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謝謝大家。
12:46
(Applause)
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(掌聲)
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